logit_nocorrection_res <- function(starts3, dat, otherdat, alts) {
    #' Full information model with Dahl's correction function
    #'
    #' Full information model with Dahl's correction function
    #'
    #' @param starts3 Starting values as a vector (num). For this likelihood,
    #'     the order takes: c([marginal utility from catch], [catch-function
    #'     parameters], [polynomial starting parameters], [travel-distance
    #'     parameters], [catch sigma]). \cr \cr
    #'     The number of polynomial interaction terms is currently set to 2, so
    #'     given the chosen degree 'polyn' there should be
    #'     (((polyn+1)*2) + 2)*(k) polynomial starting parameters, where (k)
    #'     equals the number of alternatives. The marginal utility from catch
    #'     and catch sigma are of length equal to unity respectively. The 
    #'     catch-function and travel-distance parameters are of length (# of
    #'     catch variables)*(k) and (# of cost variables) respectively.
    #' @param dat Data matrix, see output from shift_sort_x, alternatives with
    #'     distance.
    #' @param otherdat Other data used in model (as a list containing objects
    #'     `griddat`, `intdat`, `startloc`, `polyn`, and `distance`). \cr \cr
    #'     For catch-function variables (`griddat`) alternative-invariant
    #'     variables that are interacted with zonal constants to form the catch
    #'     portion of the likelihood. Each variable name therefore corresponds
    #'     to data with dimensions (number of observations) by (unity), and
    #'     returns (k) parameters where (k) equals the number of alternatives.
    #'     For travel-distance variables alternative-invariant
    #'     variables that are interacted with travel distance to form the cost
    #'     portion of the likelihood. Each variable name therefore corresponds
    #'     to data with dimensions (number of observations) by (unity), and
    #'     returns a single parameter. Any number of catch-function and
    #'     travel-distance variables are allowed, as a list of matrices. Note
    #'     the variables (each as a matrix) within `griddat` and `intdat` have
    #'     no naming restrictions. \cr \cr
    #'     Catch-function variables may correspond to variables that affect
    #'     catches across locations, or travel-distance variables may be vessel
    #'     characteristics that affect how much disutility is suffered by
    #'     traveling a greater distance. Note in this likelihood the
    #'     catch-function variables vary across observations but not for each
    #'     location: they are allowed to affect catches across locations due to
    #'     the location-specific coefficients. If there are no other data, the
    #'     user can set catch-function variables as ones with dimension
    #'     (number of observations) by (number of alternatives) and
    #'     travel-distance variables as ones with dimension (number of
    #'     observations) by (unity). \cr \cr
    #'     The variable startloc is a matrix of dimension
    #'     (number of observations) by (unity), that corresponds to the starting
    #'     location when the agent decides between alternatives. \cr \cr
    #'     The variable polyn is a vector of length equal to unity corresponding
    #'     to the chosen polynomial degree. \cr \cr
    #'     The variable distance is a matrix of dimension
    #'     (number of observations) by (number of alternatives) corresponding
    #'     to the distance to each alternative.
    #' @param alts Number of alternative choices in model as length equal to
    #'     unity (as a numeric vector).
    #' @return ld: negative log likelihood
    #' @export
    #' @examples
    #' data(zi)
    #' data(catch)
    #' data(choice)
    #' data(distance)
    #' data(si)
    #' data(startloc)
    #'
    #' optimOpt <- c(1000,1.00000000000000e-08,1,0)
    #'
    #' methodname <- 'BFGS'
    #'
    #' polyn <- 3
    #' kk <- 4
    #'
    #' si2 <- sample(1:5,dim(si)[1],replace=TRUE)
    #' zi2 <- sample(1:10,dim(zi)[1],replace=TRUE)
    #'
    #' otherdat <- list(griddat=list(si=as.matrix(si),si2=as.matrix(si2)),
    #'     intdat=list(zi=as.matrix(zi),zi2=as.matrix(zi2)),
    #'     startloc=as.matrix(startloc),polyn=polyn,
    #'     distance=as.matrix(distance))
    #'
    #' initparams <- c(3, 0.5, 0.4, 0.3, 0.2, 0.55, 0.45, 0.35, 0.25,
    #'     rep(0, (((polyn+1)*2) + 2)*kk), -0.3,-0.4, 3)
    #'
    #' func <- logit_correction
    #'
    #' results <- discretefish_subroutine(catch,choice,distance,otherdat,
    #'     initparams,optimOpt,func,methodname)
    #'
    #' @section Graphical examples: 
    #' \if{html}{
    #' \figure{logit_correction_grid.png}{options: width="40\%" 
    #' alt="Figure: logit_correction_grid.png"}
    #' \cr
    #' \figure{logit_correction_travel.png}{options: width="40\%" 
    #' alt="Figure: logit_correction_travel.png"}
    #' \cr
    #' \figure{logit_correction_poly.png}{options: width="40\%" 
    #' alt="Figure: logit_correction_poly.png"}
    #' }
    #'

    griddat <- as.matrix(do.call(cbind, otherdat$griddat))
    intdat <- as.matrix(do.call(cbind, otherdat$intdat))
    gridnum <- dim(griddat)[2]/alts
    intnum <- dim(intdat)[2]

    if (any(is.na(otherdat$noCgriddat)) == TRUE) {
    noCgridnum <- 0
    } else {
    noCgriddat <- as.matrix(do.call(cbind, otherdat$noCgriddat))
    noCgridnum <- dim(noCgriddat)[2]/alts
    }
    
    obsnum <- dim(griddat)[1]
    
    distance <- otherdat$distance
    singlecor <- otherdat$singlecor
    
    regconstant <- otherdat$regconstant
    
    starts3 <- as.matrix(starts3)
    
    revcoef <- as.matrix(starts3[1:1, ])

    gridlength <- (gridnum * alts)
        
    noCgridlength <- (noCgridnum)
    
    gridcoef <- as.matrix(starts3[2:(1 + gridlength), ])
    
    if (any(is.na(otherdat$noCgriddat)) == TRUE) {
    noCgridcoef <- 0
    } else {
    noCgridcoef <- as.matrix(starts3[(1 + 1 + gridlength):
        (1 + gridlength + noCgridlength), ])
    }
    
    signum <- 1
    
    intcoef <- as.numeric(starts3[(1 + 1 + gridlength + noCgridlength):
        ((1 + 1 + gridlength + noCgridlength) + intnum - 1), ])
    
    sigmac <- (1)
    
    # end of vector
    sigmaa <- as.matrix(starts3[((1 + 1 + gridlength + noCgridlength + 
        intnum - 1) + 1), ])
    
    sigmaa <- sqrt(sigmaa^2)
    
    gridbetas <- (matrix(gridcoef[1:(alts * (gridnum)), ], obsnum, 
        (alts * (gridnum)),
        byrow = TRUE) * cbind(griddat))
    dim(gridbetas) <- c(nrow(gridbetas), alts, (gridnum))
    gridbetas <- rowSums(gridbetas, dim = 2)
    
    if (any(is.na(otherdat$noCgriddat)) == TRUE) {
    noCgridbetas <- 0
    } else {
    noCgridbetas <- (matrix(rep(noCgridcoef, each = alts), obsnum, 
        alts * noCgridnum, byrow = TRUE) * noCgriddat)
    dim(noCgridbetas) <- c(nrow(noCgridbetas), alts, noCgridnum)
    noCgridbetas <- rowSums(noCgridbetas, dim = 2)
    }
    
    intbetas <- .rowSums(intdat * matrix(intcoef, obsnum, intnum, byrow = TRUE), 
        obsnum, intnum)
        
    if (any(is.na(otherdat$noCgriddat)) == TRUE) {
    betas <- matrix(c((gridbetas * matrix(revcoef, obsnum, alts)), intbetas),
        obsnum, (alts + 1))
    } else {
    betas <- matrix(c((gridbetas * matrix(revcoef, obsnum, alts)), intbetas),
        obsnum, (alts + 1)) + cbind(noCgridbetas, rep(0, obsnum))
    }
    
    djztemp <- betas[1:obsnum, rep(1:ncol(betas), each = alts)] *
        dat[, 3:(dim(dat)[2])]
    dim(djztemp) <- c(nrow(djztemp), ncol(djztemp)/(alts + 1), alts + 1)
    
    prof <- rowSums(djztemp, dim = 2)
    profx <- prof - prof[, 1]
    
    exb <- exp(profx/matrix(sigmac, dim(prof)[1], dim(prof)[2]))
    
    ldchoice <- (-log(rowSums(exb)))
                
    yj <- dat[, 1]
    cj <- dat[, 2]
    
    locmove <- model.matrix(~as.factor(cj) - 1)

    Xvar <- matrix(c(griddat * matrix(locmove, obsnum, gridnum * alts)), obsnum, 
        dim(gridcoef)[1])

    empcatches <- Xvar %*% gridcoef
    
    ldcatch <- matrix((-(0.5) * log(2 * pi)), obsnum) + (-(0.5) *
        log(matrix(sigmaa, obsnum)^2)) + (-(0.5) * (((yj - empcatches)/
        (matrix(sigmaa, obsnum)))^2))
    
    ld1 <- ldcatch + ldchoice
    
    ld <- -sum(ld1)
    
    if (is.nan(ld) == TRUE) {
        ld <- .Machine$double.xmax
    }
    
    ldsumglobalcheck <- ld
    assign("ldsumglobalcheck", value = ldsumglobalcheck, pos = 1)
    paramsglobalcheck <- starts3
    assign("paramsglobalcheck", value = paramsglobalcheck, pos = 1)
    ldglobalcheck <- unlist(as.matrix(ld1))
    assign("ldglobalcheck", value = ldglobalcheck, pos = 1)
    
    return(list(LL = ld, catchLL = -sum(ldcatch), choiceLL = -sum(ldchoice)))
    
}
